The availability of 3D information enables direct registration between point clouds without establishing feature correspondences. Methods like ICP [1]}, [2]}, NDT [3]} work well with proper initial guess, and global optimization approaches such as Go-ICP [4]} work without initialization requirements. These methods are widely used in point cloud based SLAM algorithms like LOAM [5]}, Cartographer [6]}, etc. Recently data driven methods like DeepICP [7]}, DeepClosestPoint [8]}, RPM-Net [9]}, etc, are also proposed.
Although these approaches do not require feature correspondences, they still rely heavily on the geometrical details of the point structures in the same modality to work well. Consequently, these approaches cannot be applied to our task on cross-modal registration.
Another group of common approaches is the two-step feature-based registration. Classical point cloud feature detectors [10]}, [11]}, [12]}, [13]} and descriptors [14]}, [15]} usually suffer from noise and clutter environments. Recently deep learning based feature detectors like USIP [16]}, 3DFeatNet [17]}, and descriptors like 3DMatch [18]}, PPF-Net [19]}, PPF-FoldNet [20]}, PerfectMatch [21]}, have demonstrated improved performances in point cloud-based registration. Similar to image-to-image registration, these approaches require feature descriptors that are challenging to obtain in cross-modality registration.
